The objective of reservoir modeling is to build 3D models of petrophysical properties (typically porosity, and permeability, and sometimes water saturation) that reservoir engineers can use to run flow simulations, forecast future hydrocarbon production and ultimate recovery, and design well development plans. In most geological environments, especially in clastic environments, porosity and permeability heterogeneity is primarily driven by facies depositional events. As such, porosity and permeability spatial distributions can be mainly characterized through the geometry and location of facies geobodies, for example sinuous sand channels. Therefore geomodelers very often first build 3D facies models (depositional facies, and sometimes lithofacies), and then populate porosity and permeability values within those models.
3D geomodels are usually built in 3D stratigraphic grids generated from a structural and stratigraphic framework, i.e. a set of interpreted faults and stratigraphic horizons. Various sources of information are used by geomodelers to build facies and petrophysical property models, including core and well log data, as well as seismic and dynamic data when available. In addition to actual reservoir data, geomodelers may borrow information from reservoir analogues, e.g., more mature reservoirs (that have more well-known characteristics) that are expected to have characteristics and features similar to the reservoir to be modeled. The modeled reservoir should typically match the well data at well data locations. This is known as well data conditioning. Conditioning to spatial trends away from well data may also be necessary.
Spatial trends, such as downwards decreasing porosity and permeability due to rock compaction or diagenesis or decreasing upwards porosity and permeability within a facies body due to waning energy in deposition, may be present in the reservoir. To account for such trends in reservoir models, petrophysical or facies input trend models need to be generated and imposed during the modeling process. 1D vertical trend curves and 2D horizontal trends maps are the most common trend models used to constrain reservoir models. Vertical trend curves provide a target petrophysical property average value or target facies proportion values in each layer of a grid of columns and layers in which a model is to be built. This may be further restricted by specific regions of the grid that are modeled separately. In each grid layer, the target property average value or the target facies proportion values can be based on a mean value of well data for that property in the layer, and edited by the modeler to address limited well data, data bias and analogue information. Furthermore, areal trend maps provide a target petrophysical average value or target facies proportion values along each column of a grid in which a model is to be built. In each column, such target values can be initialized as a mean value of well data in the column, or, if such well data is not present in the column, can be based on an interpolated average value based on previously computed columns, such as those columns including well data. This interpolation can be based, for example, on inverse distance or a kriging computation. A user, typically a geomodeler, can then edit the property areal trend map, particularly in areas away from well data. In some cases, 3D trend models can also be generated by calibrating secondary data available at each model cell, typically seismic attributes, to known well data, or by quantifying a reservoir stratigraphy interpretation. Such 3D models provide a prior local property average value, or prior local probabilities of facies occurrences in each cell of the grid in which a model is to be built. This trend model could vary from weakly informative (e.g. local proportions close to global proportions) to strongly informative and when the inter-well information supports, indicating a degree of certainty. This component of model conditioning is called an “input trend model” for the remainder of this document.
The geomodeler may opt to use any of a variety of modeling methods, such as object-based or event-based modeling to build facies models. The object-based and event-based model approaches consist in dropping objects that correspond to facies geobodies, for example sand channels, with user-specified geometries and dimensions, within the 3D grid (the space to be modeled). An iteration process is typically used to add, remove, translate, and rotate objects until the simulated objects fit to conditioning data, i.e. well data having known facies. The main difference between the object-based and event-based approaches is that event-based modeling simulates the sequence of deposition events through time by dropping objects starting from the reservoir bottom to the reservoir top according to stratigraphic rules and with surface-based models of the evolving topography, whereas object-based modeling distributes objects within the 3D grid using a purely stochastic approach. However, both approaches have the drawback of poor correlation to conditioning data, since such conditioning data is drawn from real-world observations at specific well locations and may not be consistent with the user-specified geometry and dimensions of the objects to be simulated. Even when the well data is completely consistent with the object geometries, due to the large combinatorial space of all possible objects configurations within the reservoir model, object- and event-based methods often stop short and fail to completely match well data. Mismatches between reservoir models and conditioning well data can be significant either where there is a large amount of well data having known facies, or when objects are large, typically larger than the average inter-well distance. For that reason, tolerances may be introduced in object-based or event-based modeling programs to allow models to intentionally depart from conditioning data in areas of known well data, and accelerate the modeling process. Furthermore, object-based or event-based models may depart from input trend models, especially in the case of abundant conditioning well data, or when a high level of short-scale variability is present in the input trend model.
For these and other reasons, improvements for object-based and event-based simulation methods to match dense data and/or detailed input trend models are desirable.